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@Jason: Thanks for suggesting that Google thing. While it appears to be somewhat useful I fail to see how it could answer this question since it is specifically addressing SO-users with GA/GP-experience.
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knorvOct 8 '09 at 14:52

"We expect answers to be supported by ... specific expertise...." Check! "[T]his question will likely solicit debate, arguments, polling, or extended discussion." False. There are many answers, but it's not a poll and there aren't a lot of comments or debate in the comments. Why was this closed?
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Adrian McCarthyDec 5 '12 at 17:43

Evolutionary Computation Graduate Class:
Developed a solution for TopCoder Marathon Match 49: MegaParty. My small group was testing different domain representations and how the different representation would affect the ga's ability to find the correct answer. We rolled our own code for this problem.

Neuroevolution and Generative and Developmental Systems, Graduate Class:
Developed an Othello game board evaluator that was used in the min-max tree of a computer player. The player was set to evaluate one-deep into the game, and trained to play against a greedy computer player that considered corners of vital importance. The training player saw either 3 or 4 deep (I'll need to look at my config files to answer, and they're on a different computer). The goal of the experiment was to compare Novelty Search to traditional, fitness-based search in the Game Board Evaluation domain. Results were relatively inconclusive, unfortunately. While both the novelty search and fitness-based search methods came to a solution (showing that Novelty Search can be used in the Othello domain), it was possible to have a solution to this domain with no hidden nodes. Apparently I didn't create a sufficiently competent trainer if a linear solution was available (and it was possible to have a solution right out of the gates). I believe my implementation of Fitness-based search produced solutions more quickly than my implementation of Novelty search, this time. (this isn't always the case). Either way, I used ANJI, "Another NEAT Java Implementation" for the neural network code, with various modifications. The Othello game I wrote myself.

For my undergrad thesis I used Genetic Programming to develop cooperative search strategies to be used for aerial search and rescue. I used an open source agent modelling platform called NetLogo (based on StarLogo) as the world model. NetLogo is written in java and thus provides java APIs - so the GP framework needed to be based on java - the one I used is called JGAP there is also another open source GP framework in java I know of called ECJ.

The simulations were quite slow to run (I think this is due to the NetLogo model) so my function/terminal sets were quite restricted, limiting the search space.t Despite this, I came up with some good solutions. If you feel the urge, you can read about it in chapter 3 of my thesis http://www.cse.unsw.edu.au/~ekjo014/z3157867_Thesis.pdf